import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px

from urllib.request import urlopen
import json    

# with urlopen('https://raw.githubusercontent.com/Subhash9325/GeoJson-Data-of-Indian-States/master/Indian_States') as response:
#     counties = json.load(response)

with urlopen('https://raw.githubusercontent.com/nikhilkumarsingh/choropleth-python-tutorial/master/states_india.geojson') as response:
    counties = json.load(response)

print(counties["features"][0].keys())
# Note that the loaded geoJSON must have an outermost key id. Note key id is not present here

dict_keys(['type', 'geometry', 'properties'])

for i in range(len(counties['features'])):
    # Loop to add 'id' key
    counties['features'][i]['id']=counties['features'][i]['properties']['st_nm']
#     counties['features'][i]['id']=counties['features'][i]['properties']['NAME_1']
    
print(counties["features"][0].keys())

dict_keys(['type', 'geometry', 'properties', 'id'])

names = []
for i in range(len(counties['features'])):
    names.append(counties['features'][i]['id'])

df = pd.read_csv("data/NCRB_ADSI-2020_Table_1A.9.csv")
df = df[(df['Category']=='State') + (df['Category']=='Union Territories')]

Note : In geoJSON data, there is no separate data for Ladakh and Jammu Kashmir. However, in csv file there are two separate rows. Therefore merging the rows of Ladakh and Jammu Kashmir in dataframe. Also in geoJSON file there are two places 'Daman & Diu' and 'Dadara & Nagar Havelli', however, there is only one row named 'D & N Haveli and Daman & Diu' in dataframe. Therefore a copy of that row is made and used for both the places.

id1=df.index[df['State/UT/City'] == 'Jammu & Kashmir'].tolist()[0]
id2=df.index[df['State/UT/City'] == 'Ladakh'].tolist()[0]
temp = df.loc[[id1,id2]]
temp = pd.concat([temp, (temp.sum(axis=0)).to_frame().transpose()])
temp = temp.drop(labels=[id1,id2],axis=0)
temp = temp.rename(index={0:id1})
temp.loc[id1,'Si. No.'] = str(id1)
temp.loc[id1,'Category'] = 'Union Territories'
temp.loc[id1,'State/UT/City'] = 'Jammu & Kashmir & Ladakh'
df = df.drop(labels=[id1,id2])
df = pd.concat([df,temp])

id1=df.index[df['State/UT/City'] == 'D & N Haveli and Daman & Diu'].tolist()[0]
temp = df.loc[id1].to_frame().transpose()
temp['State/UT/City']='D & N Haveli and Daman & Diu-2'
df = pd.concat([df,temp])

df = df.sort_values('State/UT/City', axis=0)
df.head()

	Si. No.	Category	State/UT/City	Dangerous or Careless Driving/ Over-taking/etc. - Cases	Dangerous or Careless Driving/ Over-taking/etc. - Injured	Dangerous or Careless Driving/ Over-taking/etc. - Died	Over Speeding - Cases	Over Speeding - Injured	Over Speeding - Died	Driving under Influence of Drug/Alcohol - Cases	...	Other Causes - Died	Total Road Accidents - Cases	Total Road Accidents - Injured	Total Road Accidents - Died	Unmanned Railway Crossing Accidents - Cases	Unmanned Railway Crossing Accidents - Injured	Unmanned Railway Crossing Accidents - Died	Grand Total - Cases	Grand Total - Injured	Grand Total - Died
29	29	Union Territories	A & N Islands	82	90	8	26	31	2	33	...	0	141	145	14	0	0	0	141	145	14
0	1	State	Andhra Pradesh	3300	3630	1221	12344	13774	4778	154	...	668	17924	19675	7039	0	0	0	17924	19675	7039
1	2	State	Arunachal Pradesh	25	15	15	39	32	23	0	...	19	159	143	90	0	0	0	159	143	90
2	3	State	Assam	1169	893	462	3293	2530	1377	491	...	33	6737	5065	2813	1	0	1	6738	5065	2814
3	4	State	Bihar	2564	1936	1881	3674	3072	2785	152	...	150	8639	7019	6698	0	0	0	8639	7019	6698

5 rows × 51 columns

df_cols = list(df.columns)
for i in range(3,len(df_cols)):
    df[df_cols[i]]=df[df_cols[i]].astype('int64')   

names.remove('NCT of Delhi')
names.append('Delhi')
names.sort()

# Create a dictionary to map state name of dataframe to geo location
df2geo = {}
df_name = list(df['State/UT/City'])
for i in range(df.shape[0]):
    df2geo[df.iloc[i,2]]=names[i]
    
df2geo['Delhi (UT)']='NCT of Delhi'
df['name2geo']=df['State/UT/City'].map(df2geo)

fig = px.choropleth_mapbox(df, geojson=counties, locations='name2geo', 
                           color='Grand Total - Died',
                           color_continuous_scale="Viridis",
                           mapbox_style="carto-positron",
                           zoom=3, center = {"lat": 20.5937, "lon": 78.9629},
                           opacity=0.5
                          )
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})

fig.show()

fig.write_html("../_html/map_totalDied.html")